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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Cooperative Load Scheduling for Multiple Aggregators Using Hierarchical ADMM

Demand response (DR) serves an important role in improving the efficiency and stability of power systems. In recent years, with advances in communication and smart device technologies, many aggregators have emerged to facilitate end customer participation in DR programs. These aggregators, equipped with customized optimal control algorithms, are capable of providing various grid services. Among them is load scheduling during DR events, namely following a load signal provided by the utility company while minimizing overall customer discomfort. However, as the number of aggregators keeps increasing, it becomes challenging for utility companies to conduct load scheduling for multiple aggregators and generate reference signals for each of them. This paper proposes an optimization framework using hierarchical alternating direction method of multipliers (H-ADMM) to optimally generate load following signals for multiple aggregators. Under this framework, utility and multiple aggregators work in a cooperative manner, aiming at minimizing an overall system cost from different levels of the power system hierarchy, while protecting user privacy. A case study has been conducted in a system with multiple aggregators, based on control of HVAC loads. Experimental results validate the effectiveness of the proposed algorithm.

ADMM↗
DOE OSTI2020

Cooperative Load Scheduling for Multiple Aggregators Using Hierarchical ADMM: Preprint

Demand response (DR) serves an important role in improving the efficiency and stability of power systems. In recent years, with advances in communication and smart device technologies, many aggregators have emerged to facilitate end customer participation in DR programs. These aggregators, equipped with customized optimal control algorithms, are capable of providing various grid services. Among them is load scheduling during DR events, namely following a load signal provided by the utility company while minimizing overall customer discomfort. However, as the number of aggregators keeps increasing, it becomes challenging for utility companies to conduct load scheduling for multiple aggregators and generate reference signals for each of them. This paper proposes an optimization framework using hierarchical alternating direction method of multipliers (H-ADMM) to optimally generate load following signals for multiple aggregators. Under this framework, utility and multiple aggregators work in a cooperative manner, aiming at minimizing an overall system cost from different levels of the power system hierarchy, while protecting user privacy. A case study has been conducted in a system with multiple aggregators, based on control of HVAC loads. Experimental results validate the effectiveness of the proposed algorithm.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Local and utility-wide cost allocations for a more equitable wildfire-resilient distribution grid

Climate-induced extreme weather conditions make electricity infrastructure more vulnerable. They increase the risk of power-line-ignited wildfires which can, in turn, jeopardize electric power delivery. Here, leveraging machine learning, we show that lower-income communities in California not only have lower fractions of power distribution lines undergrounded, but overhead lines and poles in their neighbourhoods are also more vulnerable to wildfires. Should they bear the cost of undergrounding fire-prone lines themselves, they would have to pay a disproportionately higher cost per household. We propose a cost allocation scheme with an income threshold below which the cost is borne by utility-wide ratepayers and above which the cost is borne locally. This scheme can not only minimize the average of undergrounding costs per household as a share of income, but also homogenize such cost–income ratios across communities. Furthermore, our research demonstrates the opportunity to appropriately integrate existing policies to make electricity infrastructure affordable, equitable and reliable amidst climate change.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

ActiveBAS: A Low-cost, Scalable Control Solution for Grid-Interactive Small and Medium Sized Commercial Buildings

This project aims to develop and enhance a low-cost, highly scalable control solution for Small and Medium-Sized Commercial Buildings (SMCB), assess the business potential at multiple sites, and perform commercialization efforts. The technology can be applied to any buildings served by multiple units, with the benefits being greatest for open-spaced buildings, such as banks, retail stores, restaurants, and factories. This project aims to develop an affordable control solution for: 1) SMCB grid responsiveness, 2) reduction of GHG by changing unit operations, 3) greater reduction in utility costs, and 4) rapid adoption in the marketplace. The proposed technology will be built on a previously developed and demonstrated MPC solution. The minimal sensor requirement and less need of control expertise are the unique feature of the algorithm that leads to low capital and maintenance costs, and short installation and implementation time. These attributes contribute to low capital and maintenance costs, as well as a short installation and implementation time. However, these advantages come with a trade-off: increased difficulties and unreliability when applying traditional modeling and MPC control approaches due to limited information. This final report describes the modeling approaches developed and tested to overcome these challenges. It begins by outlining the modeling challenge posed by minimal sensor requirements, then delves into the proposed modeling approaches, which primarily involve system identification. Finally, preliminary test results for a simulation case study are presented.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Assessing the Expansion of Ground-Motion Sensing Capability in Smart Cities via Internet Fiber-Optic Infrastructure

Monitoring ground motion in smart cities can improve the public safety by providing critical insights on natural and anthropogenic hazards, for example, earthquakes, landslides, explosions, infrastructure failures, and so forth. Although seismic activity is typically measured using dedicated point sensors (e.g., geophones and accelerometers), techniques such as distributed acoustic sensing have demonstrated the utility of using fiber-optic cable to detect seismic activity over comparable distances. In this article, we present the results of a study that quantifies the expansion in an area monitored for low-amplitude ground-motion events by augmenting existing point sensors with the internet fiber-optic cable infrastructure. Here we begin by describing our methodology, which utilizes geospatial data on point sensors and internet optical fiber deployed in metropolitan statistical areas (MSAs) in the United States. We extend these data to identify the area that can be monitored by (1) considering the observed seismic noise data in target locations, (2) applying the model from Wilson et al. (2021) to understand the potential coverage area gains using optical fiber sensing, and (3) optimizing the selection of fiber segments to maximize coverage and minimize deployment costs. We implement our methodology in ArcGIS to assess the additional area that can be monitored for low-amplitude ground-motion events (i.e., magnitude >0.5) by utilizing internet fiber-optic cables in the 100 most populous MSAs in the United States. We find that the addition of internet fiber-based sensors in MSAs would increase the area monitored on average by over an order of magnitude from 1% to 12%, if the subset of fiber cable segments that maximize coverage and minimize deployment costs is chosen even if only 20% of all fibers are used.

58 GEOSCIENCES↗
DOE OSTI2022

Analysis of precooling optimization for residential buildings

To reduce peak demand and/or energy cost for residential buildings, optimal precooling strategies are becoming important as an alternative to rule-based precooling strategies that are intuitive but may not be optimal. Since precooling optimization is heavily dependent on a variety of factors such as the home thermal properties, HVAC system, weather, thermal comfort criteria, and utility rate structure, the individual and collective impact of these factors on precooling performance needs to be analyzed. In addition, since the indoor air temperature is affected by heat transfer to and from the interior wall surface, performance analysis in view of the interior wall surface temperature is also essential. Therefore, in this paper, an optimal precooling strategy that accounts for the aforementioned factors and utilizes a second-order thermal network model, is proposed. With this strategy, the HVAC on/off control signal that minimizes 24-hour energy cost while maintaining thermal comfort, is determined. Through extensive simulations, it is found that the proposed optimal precooling strategy is able to adapt to changing conditions and that having a sufficiently low interior wall surface temperature during precooling is critical for avoiding expensive on-peak operation. Here, the reason for the latter is that such a temperature indicates that enough “cooling energy” has been stored. It is also found that weather has the most dominant impact on the precooling performance, followed by home thermal condition, with the rated cooling capacity and utility rate structure having the least impact.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Accelerated CO2 Storage Optimization Using Multi-Resolution Fourier Neural Operator at the Illinois Basin Decatur Project (IBDP)

This paper presents a deep learning-based approach for optimizing CO2 injection in carbon capture and storage (CCS) operations. We developed a multi-resolution machine learning model to significantly reduce data generation costs. Utilizing this proxy model, we implemented a multi-objective genetic algorithm to optimize well control during the CO2 injection process. The proposed approach was applied to the Illinois Basin Decatur Project (IBDP), successfully optimizing the CO2 injection schedule based on three key objectives: maximizing the amount of CO2 stored, maximizing sweep efficiency, and minimizing pressure increase. The use of the proxy model accelerated the optimization workflow by two orders of magnitude, while the cost of data generation for the proxy model was reduced by 90% by utilizing a coarse-scale model.

accelerated CO2 storage optimization↗
DOE OSTI2025

Dynamic Charging Rendezvous and Motion Planning for a Multi-AGV Team Including a Mobile Charging Host

Teams of automated battery-powered electric vehicles have the potential to execute complex mission tasks in off-road environments for agriculture, military, and other applications. Limited onboard energy reserves hinder their adoption in large-scale resource-constrained environments, where recharging is a necessity. It may be infeasible to install a network of static charging stations in off-road environments. For this reason, dedicated mobile host vehicles with charging capabilities are proposed as a means to increase range and capabilities of the multivehicle team. Here, in this study, we consider an ad hoc planning framework, where results from a high-confidence trajectory planner are leveraged to plan charging rendezvous between a host and other worker vehicles in a receding horizon fashion to provide high confidence that energy reserves will not be prematurely exhausted. The core problem is posed so as to minimize the impact of recharging on the mission in terms of task delays, overall energy utilization, and costs of fast charging. Through extensive Monte Carlo simulations of an off-road mission, we show a decrease in task delays without substantial increases in energy needs by updating the charging rendezvous plan during the mission. However, if updates are made too often, model mismatch may cause unnecessary cycling and mission failure.

Energy constraints↗
DOE OSTI2023

Stabilizing the Grid and Reducing Utility Bills Through Price-Responsive Controls for Heat Pump Water Heaters

The electricity grid is facing increasing challenges in cost-effectively balancing supply and demand. These challenges are exacerbated by increased penetration of photovoltaics, which causes mid-day overproduction, and electrification of gas appliances, which increases peak-period electricity demand. Decarbonization requires shifting building loads from fossil-intensive high-cost times to renewable-intensive low-cost times while maintaining quality of service to occupants. Utilities and ISO’s are investigating new ways of incentivizing this load shifting. One promising method is the use of Highly Dynamic Prices (HDPs). HDPs feature continuously changing prices that reflect real-time grid generation and distribution costs and capacity constraints, and thus incentivize consumers to shift their loads. California’s CPUC CalFUSE proposal and Hawaii’s recent changes demonstrate that electricity tariffs are moving towards this model. For this to work however, loads must have the capability to respond to these prices. Heat pump water heaters (HPWHs) are an ideal device for this purpose because their storage tanks decouple delivery of domestic hot water from electricity consumption. The storage enables control strategies that consume midday solar power to increase the energy stored in the tank, then provide evening peak domestic hot water services using the stored energy. Berkeley Lab's CalFlexHub project is pioneering price-driven load flexibility by developing and deploying cost-minimizing controls for many flexible loads - including HPWHs - in response to HDP. Control development is based on simulations using the Flexible Heat Pump Water Heater Performance Predictor which captures the control decisions of the on-board controller in a residential, integrated HPWH . The price-responsive controls a) shift load in ways that consume additional midday solar power to help stabilize the grid and reduce overall emissions, b) ensure that occupants receive equal or better hot water delivery service, and c) minimize the operating cost for each home in the fleet. On the grid level, the resulting shift will reduce utility operating costs and emissions, and can avoid expensive system capacity expansions. The control approach is customized to each home based on typical hot water consumption patterns. HPWH controllers, whether on the device or remotely, will receive a schedule of CTA-2045-B signals or set temperature adjustments customized to the current HDP price schedule and home. Simulation results for a fleet of 148 HPWHs on a summer day in Berkeley, California show cost savings of 29% and high price electricity consumption reductions of 80%, while maintaining full quality of service.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Impacts of Ionizing Radiation on Sulfur Chloride Compounds for Advanced Fuel Cladding Decontamination Processes

Tripling the nation’s nuclear energy capacity is a critical component for significantly increasing energy production and reducing energy costs for American families and businesses. Achieving this vision requires fuel cycle technologies that maximize resource utilization while minimizing radioactive waste generation. Advanced sulfur chloride–based chlorination technologies are being developed to enable efficient recycling of fuel cladding materials, which account for a significant fraction of used nuclear fuel. However, the impacts of ionizing radiation on the longevity and performance of these sulfur chloride compounds are not well established. Here, we will explore the effects of gamma and electron-beam irradiation on the chemical composition of select sulfur chloride reagents, specifically sulfur monochloride (S2Cl2) and thionyl chloride (SOCl2), and the impacts of pre-irradiation on the chlorination yield/chemical dissolution of surrogate aluminum alloy 6061 (AA6061-T6) materials.

38 - RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCL↗
DOE OSTI2021

Disaggregating growth in future retail electricity rates

The retail rate impacts of a number of emerging trends (e.g., rapid deployment of electric vehicles and storage, transmission build-out for large-scale renewables deployment, and grid modernization) are unknown. Importantly, decision-makers are concerned about the potential future rate impacts on energy affordability and equity. We disaggregate the key drivers of retail electricity rates and assess their impacts on future rate growth considering their interactions and uncertainty. Specifically, we develop ranges of future cost growth for a generic investor-owned and vertically-integrated electric utility representing typical cost and operating characteristics. The rate driver growth rate ranges are applied in isolation and jointly to quantify the uncertainty and variability in future retail electricity rates. The results identify what rate drivers and factors may minimize and/or decrease uncertainty in retail rate growth and their linkages to industry trends.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Direct Air Capture of CO 2 and Delivery to Photobioreactors for Algal Biofuel Production (Final Report)

A mobile DAC system was designed and constructed to pair with photobioreactors growing algae for biofuel production. The DAC system was designed as a versatile research system, rather than a compact production unit. The system was constructed and mounted on a mobile skid to facilitate transportation to the algae production site. Within the DAC system, CO 2 was captured using amine-loaded monoliths that allow for high CO 2 uptake with low pressure drop. The CO 2 is collected using a Global Thermostat patented temperature/vacuum swing adsorption (TVSA) process. Amine sorbents and process conditions were optimized to produce 10 to >20 g CO 2 .h-1. The stability of the amine sorbents was also studied, with sorbent modifications made to improve stability to degradation by oxidation. An Algenol-developed Spirulina strain (Arthrospira platensis AB2293) was selected as the production cyanobacterial strain. AB2293 cultured was inoculum for outdoor production following PBR installation by Algenol. The PBR system was composed of three independent PBRs, with each PBR composed of four hanging bags internally recirculated by a liquid turnover pump. The PBRs were operated outdoors in Atlanta, GA, and integrated with the DAC system. Algae were grown with similar productivity using DAC-CO 2 as algae grown using pure CO 2 obtained commercially (Airgas). Throughout the experimental duration, no discoloration was observed, and cellular morphology was consistent between the two experimental treatments. An LCA including lifecycle greenhouse gas emissions, full life cycle inventory of the Algenol system and the DAC system and integrated DAC+PBR system was developed. Lifecycle greenhouse gas emissions were calculated for capture of carbon dioxide using input from Global Thermostat and the National Renewable Energy Laboratory. Three scenarios for energy provision were evaluated: a natural gas combined heat and power system sized to meet the electricity requirement, a natural gas combined heat and power system sized to meet the process heat requirements, and a system without on-site power that procures the electricity from the grid. In all three cases, as expected, the major contributor to the emissions is the energy consumption associated with the desorption step of the DAC process. The LCA quantified the reduced potential energy and greenhouse gas emissions of heat and mass integration of DAC and Algenol compared to unintegrated DAC and Algenol systems. A life cycle assessment of the role of sorbent productivity and lifetime was also developed. The development of more robust, oxidation resistant DAC sorbents may enable small reductions in energy requirements and in lifecycle greenhouse gas emissions and other environmental impacts. NREL performed techno-economic analysis (TEA) to identify the integration scenario most likely to achieve a 15% cost reduction target versus the baseline. Heat and mass integration of DAC and the PBR is critical to minimizing the MFSP. The baseline case utilizes no heat and mass integration, and the DAC system provides 100% of the CO 2 required by the photobioreactors (20 tonnes/hr), operating for 12 hours/day capturing 40 tonnes CO 2 /operating hour. The minimum fuel selling price (MFSP) of ethanol calculated from the baseline case was $10.68/gal ethanol. This corresponds with a targeted MFSP of $9.07/gal ethanol (or 15% reduction). This target was achieved by integration Option 2a with the greatest cost reduction of 17.8% (or $8.78/gal) and integration Option 2b with a cost reduction of 16.4% (or $8.93/gal). Reductions in MFSP are attributed to two primary process considerations: (a) CO 2 storage at night reduces the capital expenses associated with DAC (i.e., increasing on-stream time); and (b) distributed DAC scenarios (DAC-PBR integration Options 2a and 2b) make use of boiler and DAC CHP flue gas CO 2 (free). Direct air capture on-stream time was one of the largest contributors to MFSP reduction.

09 BIOMASS FUELS↗
DOE OSTI2024

Direct Air Capture and Utilization System (DACUS): FEED Study of CarbonCapture Inc. DAC and CarbonCure Utilization Technologies Using United States Steel’s Gary Works Plant Waste Heat

The effects of climate change are drastically perceived through the alarming rates of severe events such as flooding, hurricanes, wildfires, global temperature rise, sea-level rise, and ocean acidification etc., which has prompted the nations across the globe to control & reduce greenhouse gas (GHG) emissions, by massive adoption of electric vehicles, switching to renewal energy sources and promoting sustainable green products. However, recent climate studies have shown that such efforts will not be enough to keep the global temperature rising under 1.5° Celsius by the year 2050, which lead to the development of multiple Direct Air Capture (DAC) technologies. A DAC system extracts CO2 directly from the atmosphere, which is either stored underground for long term or utilized in other processes. The DACUS proposed at U.S. Steel Corporation’s Gary Works Plant in Gary, Indiana, will include an innovative DAC technology from CarbonCapture Inc. (CCI), which will be collocated and powered by waste heat from the steel plant to capture CO2 from ambient air, and CarbonCure CO2 utilization technology- which is a commercial technology that is already installed and in use by early adopters in more than 150 concrete mixing plants across North America and Asia. The University of Illinois at Urbana Champaign spearheaded this Front-End Engineering Design (FEED) study project, which was funded by US Department of Energy (DOE), aimed at designing a state-of-the art DACU system that can remove minimum of 5,000 tonne/year net CO2 from air (based on cradle-to-gate LCA) and converting the captured CO2 into low carbon intensity products. The off takers of the low Carbon product produced by the DACUS have also been identified near the steel plant in the Indiana, Illinois & Wisconsin Region. This creates a full CO2 value chain and provides a means to assess the impact of this holistic approach on job creation, regional economic impact, and environmental justice. DACUS is a promising new technology for reducing greenhouse gas in the atmosphere. Operation costs are minimized by locating CCI’s innovative DAC collector at United States Steel's Gary Works site where waste heat is utilized. Carbon emissions are minimized in CO2 transportation. The captured CO2 will be permanently sequestered in concrete used as construction material. This DOE funded FEED study demonstrates a full CO2 value chain for a DACU system, supported by an industrial facility. If this project were constructed, it would have a very positive and lasting impact on both the environment and the community.

42 ENGINEERING↗
DOE OSTI2022

Analysis of predicted mean vote-based model predictive control for residential HVAC systems

Model Predictive Control (MPC) is an advanced process control method that has attracted much attention in building heating, ventilation, and air conditioning (HVAC) systems. Here, this paper analyzes the optimal precooling performance in residential buildings using MPC with two different comfort indices, namely, temperature and predicted mean vote (PMV). It first formulates, for each comfort index, an optimization problem that accounts for different factors, such as weather, home thermal condition, prediction horizon, time-of use (TOU) utility rate, and rated cooling capacity. The problem is then solved, resulting in an MPC strategy that determines the HVAC on/off control signal and minimizes energy cost over a receding time horizon while maintaining thermal comfort. The energy performance difference between temperature-based and PMV-based MPC strategies is subsequently investigated, especially in light of the interior wall surface temperature and under different combinations of the factors. Extensive simulation results demonstrated that the proposed MPC strategies are adaptive and their performances depend primarily on weather, home thermal condition, and prediction horizon, while the impact of TOU utility rate and rated cooling capacity is relatively small. Because the PMV-based MPC strategy can take advantage of the lower interior wall surface temperature due to precooling, it resulted in 8–45% cost savings for the scenarios investigated and an average increase of 0.042–0.113 in the absolute value of the PMV index compared to the temperature-based MPC strategy.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Whole Algae Hydrothermal Liquefaction and Upgrading: A review of progress and challenges and insight into the future

This report summarizes the research at Pacific Northwest National Laboratory (PNNL) to evaluate the economic viability and environmental impact of using microalgae to produce fuels and other products via hydrothermal liquefaction (HTL). Over the past several years, PNNL has examined key aspects of feedstock cost and availability, formatting and conversion techniques, and the utilization of all HTL products. Investigations of feedstock cost led to opportunities to work with cost-advantaged algal feedstocks that can be provided at minimal cost for HTL processing. Cost-advantaged algae include wastewater-grown algae and harvested algal blooms. Although farm-cultivated algae offer the best possible biomass composition and scalability for HTL processing, the cost of the feedstock is too high to yield an economically competitive biofuel. Processing cost-advantaged feedstocks creates other unique challenges in adapting HTL to upgrade biomass with higher than typical ash content and less preferred composition (low lipid). Despite the challenges, HTL of cost-advantaged algae results in economically competitive pricing scenarios and significant advantages in reducing net emissions below 70% of the petroleum baseline. The utilization of a variety of potential non-fuel products from algal HTL, such as the use of HTL solids as a cement additive, provides a significant reduction in net emissions by offsetting emissions from other carbon-intense products. This report presents an analysis of the research conducted at PNNL to develop an economically and environmentally beneficial process for algae HTL.

09 BIOMASS FUELS↗
DOE OSTI2024

Aerosol Envelope Sealing of Existing Residences

This report explores the best methods for aerosol envelope sealing of unoccupied, existing residences and documents typical leakage reductions. The project consisted of three distinct efforts: (1) scaled field demonstrations of the sealing process, tracking from setup to sealing and cleanup, (2) laboratory testing of new sealants that dry clear, making them more appropriate for retrofit applications, and (3) BEopt™ (Building Energy Optimization Tool) modeling of the energy implications of the measured reductions in leakage. Overall, aerosol sealing performance in existing homes was effective, with an average leakage reduction of 47% across all 34 sites. Conventional approaches typically only produce leakage reductions of 25%–30%. The aerosol sealing technology could provide a process for existing homes and multifamily units to gain the benefits of a well-sealed home at a reasonable cost with minimal disruption.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Evaluation of microalgae cultivation at air-CO 2 equilibrium pH for improving carbon utilization efficiency

Microalgae are often cultivated at near-neutral pH to optimize growth. However, this results in significant CO 2 loss through outgassing in open-pond cultivation systems, leading to low CO 2 utilization efficiency and higher biomass production costs. One potential solution is algal cultivation at air-CO 2 equilibrium pH, which minimizes CO 2 outgassing but may inhibit growth due to stresses at higher pH levels. In this study, we evaluated the viability of this approach by growing Picochlorum celeri and Tetraselmis striata under outdoor relevant conditions at equilibrium pH. Compared to pH 7 cultures, biomass productivity declined by 35 % for P. celeri and 57 % for T. striata. Although CO 2 outgassing could be minimized, the significant loss in productivity led to a higher overall minimum biomass selling price (MBSP). Increased ammonia toxicity at the higher pH was found to be one of the growth-limiting factors and was mitigated by reducing ammonium fertilizer concentration. This adjustment resulted in a more moderate productivity decline of 13 % (instead of 35 %) for T. striata, and consequently, a lower MBSP was achieved. These findings suggest that equilibrium pH cultivation may be a viable method for reducing CO 2 loss without significantly compromising biomass productivity. Depending on the strain, strategies to mitigate stressors, such as ammonia toxicity, may be necessary.

09 BIOMASS FUELS↗
DOE OSTI2023

Techno-economic optimization of renewable urea production for sustainable agriculture and CO 2 utilization

Abstract Urea is the most used nitrogen fertilizer due to its ease of storage, transportation, and application. It is made by combining ammonia and carbon dioxide (CO 2 ), both of which are produced predominantly from fossil fuels at present. The recent momentum behind ammonia production using renewable-powered electrolysis offers an opportunity to both make urea in a more sustainable way and utilize CO 2 from external sources. In this work, we present a techno-economic optimization model to minimize the cost of making urea in this way. The model allows for time-varying chemical production in response to renewable variability by simultaneously optimizing production facility design and hourly operation. We performed a case study for Minnesota considering the use of byproduct CO 2 from bioethanol production. We found that the present-day levelized cost of renewable urea is between $268 mt −1 and $413 mt −1 at likely implementable production scales up to 250 000 mt yr −1 . This is within the range of historical conventional urea prices while offering at least 78% carbon intensity reduction. Projecting to 2030, there is a clear economic case for renewable urea production with levelized cost as low as $135 mt −1 due to technology improvement and electrolysis manufacturing expansion, facilitating a urea production scale increase to 525 000 mt yr −1 . Optimal facilities use wind energy, with hydrogen and ammonia production operating in a flexible, time-varying way to minimize battery and hydrogen storage capacities. Urea production operates near steady state due to the relatively low cost of intermediate ammonia buffer storage. A mix of imported methane and locally produced hydrogen are used to provide heat for steam consumed in the urea synthesis.

Chemistry↗